fix some typos

src/treelearner/leaf_splits.hpp

@@ -10,7 +10,7 @@
 namespace LightGBM {
 
 /*!
-* \brief used to find splits candidates for a leaf
+* \brief used to find split candidates for a leaf
 */
 class LeafSplits {
 public:
@@ -26,7 +26,7 @@ public:
   }
 
   /*!
-  * \brief Init splits on current leaf, don't need to travesal all data
+  * \brief Init split on current leaf on partial data. 
   * \param leaf Index of current leaf
   * \param data_partition current data partition
   * \param sum_gradients
@@ -43,7 +43,7 @@ public:
   }
 
   /*!
-  * \brief Init splits on current leaf, need to travesal all data to sum up
+  * \brief Init splits on current leaf, it will travese all data to sum up the results
   * \param gradients
   * \param hessians
   */
@@ -66,7 +66,7 @@ public:
   }
 
   /*!
-  * \brief Init splits on current leaf, need to travesal all data to sum up
+  * \brief Init splits on current leaf of partial data.
   * \param leaf Index of current leaf
   * \param data_partition current data partition
   * \param gradients

src/treelearner/parallel_tree_learner.h

@@ -14,8 +14,8 @@ namespace LightGBM {
 
 /*!
 * \brief Feature parallel learning algorithm.
-* Different machine will find best split on different features, then sync global best split
-* When #data is small or #feature is large, you can use this to have better speed-up
+*        Different machine will find best split on different features, then sync global best split
+*        It is recommonded used when #data is small or #feature is large
 */
 class FeatureParallelTreeLearner: public SerialTreeLearner {
 public:
@@ -39,8 +39,8 @@ private:
 
 /*!
 * \brief Data parallel learning algorithm.
-* Workers use local data to construct histograms locally, then sync up global histograms.
-* When #data is large or #feature is small, you can use this to have better speed-up
+*        Workers use local data to construct histograms locally, then sync up global histograms.
+*        It is recommonded used when #data is large or #feature is small
 */
 class DataParallelTreeLearner: public SerialTreeLearner {
 public: